Although soft-sensors have been widely used for estimating product quality or other key variables, they do not always function well in practice due to changes in process characteristics. Correlation-based Just-In-Time (CoJIT) modeling has been proposed to cope with changes in process characteristics. In CoJIT modeling, the samples used for local modeling are selected on the basis of correlation together with distance, since changes in process characteristics are expressed as the difference of the correlation. In addition, the individuality of production devices should be considered when they are operated in parallel. However, CoJIT modeling cannot cope with the individuality of production devices because it is only applicable to time-series data. In the present work, a new pattern recognition method, referred to as Nearest Correlation (NC) method, is proposed, and it selects samples whose correlations are similar to the query. In addition, the proposed NC method is integrated with CoJIT modeling. The advantages of the proposed CoJIT modeling with NC method are demonstrated through a case study of a parallelized CSTR process.